High-Speed Receiver Architecture

ABSTRACT

A receiver (e.g., for a 10G fiber communications link) includes an interleaved ADC coupled to a multi-channel equalizer that can provide different equalization for different ADC channels within the interleaved ADC. That is, the multi-channel equalizer can compensate for channel-dependent impairments. In one approach, the multi-channel equalizer is a feedforward equalizer (FFE) coupled to a Viterbi decoder, for example a sliding block Viterbi decoder (SBVD); and the FFE and/or the channel estimator for the Viterbi decoder are adapted using the LMS algorithm.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a divisional application of U.S. patent applicationSer. No. 13/013,149, “High-Speed Receiver Architecture,” filed Jan. 25,2011 by Oscar E. Agazzi, et al., now U.S. Pat. No. 8,831,074, whichclaims priority under 35 U.S.C. §119(e) to U.S. Provisional PatentApplication No. 61/298,125, “Use of the Offset Method and/or theV-Method to Compensate for Nongaussian and Signal Dependent Noise,”filed Jan. 25, 2010 by Mario R. Hueda and Diego E. Crivelli; and U.S.Provisional Patent Application No. 61/320,310, “Techniques to ImproveCL101X Performance in Transmissions over SMF Channels,” filed Apr. 13,2010 by Mario R. Hueda. U.S. patent application Ser. No. 13/013,149 isalso a continuation-in-part of U.S. Utility patent application Ser. No.12/966,987, “High-Speed Receiver Architecture,” filed Dec. 13, 2010 byOscar E. Agazzi et al., now abandoned, which is a continuation of U.S.Utility patent application Ser. No. 11/559,850, “High-Speed ReceiverArchitecture,” filed Nov. 14, 2006 by Oscar E. Agazzi et al, now U.S.Pat. No. 7,852,913. U.S. Utility patent application Ser. No. 11/559,850claims priority under 35 U.S.C. §119(e) to U.S. Provisional PatentApplication No. 60/737,103, “EDC Transceiver: System and ChipArchitecture,” filed Nov. 15, 2005 by Oscar E. Agazzi et al.; U.S.Provisional Patent Application No. 60/779,200, “MIMO/MLSE Receiver forElectronic Dispersion Compensation of Multimode Optical Fibers,” filedMar. 3, 2006 by Oscar E. Agazzi et al.; and U.S. Provisional PatentApplication No. 60/783,344, “MIMO/MLSE Receiver for ElectronicDispersion Compensation of Multimode Optical Fibers,” filed Mar. 16,2006 by Oscar E. Agazzi et al. U.S. Utility patent application Ser. No.11/559,850 is also a continuation-in-part of U.S. Utility patentapplication Ser. No. 11/538,025, “Multi-Channel Equalization toCompensate for Impairments Introduced by Interleaved Devices,” filedOct. 2, 2006 by Oscar E. Agazzi et al., now U.S. Pat. No. 7,778,320,which claims priority under 35 U.S.C. §119(e) to U.S. Provisional PatentApplication Ser. No. 60/723,357, “Compensation Of Track And HoldFrequency Response Mismatches In Interleaved Arrays of Analog to DigitalConverters for High-Speed Communications Receivers,” filed Oct. 3, 2005by Oscar E. Agazzi et al. U.S. Utility patent application Ser. No.11/559,850 is also a continuation-in-part of U.S. Utility patentapplication Ser. No. 11/551,701, “Analog-to-Digital Converter UsingLookahead Pipelined Architecture and Open-Loop Residue Amplifiers,”filed Oct. 20, 2006 by Carl Grace, now U.S. Pat. No. 7,576,676, whichclaims priority under 35 U.S.C. §119(e) to U.S. Provisional PatentApplication Ser. No. 60/764,866, “ADC Provisional Patent Application,”by Carl Grace, filed Feb. 2, 2006. The subject matter of all of theforegoing is incorporated herein by reference in their entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to high speed data communications.

2. Description of the Related Art

Optical fiber is widely used as a communications medium in high speeddigital networks, including local area networks (LANs), storage areanetworks (SANs), and wide area networks (WANs). There has been a trendin optical networking towards ever-increasing data rates. While 100 Mbpswas once considered extremely fast for enterprise networking, attentionhas recently shifted to 10 Gbps, 100 times faster. As used in thisapplication, 10 Gigabit (abbreviated as 10G or 10 Gbps or 10 Gbit/s)systems are understood to include optical fiber communication systemsthat have data rates or line rates (i.e., bit rates including overhead)of approximately 10 Gigabits per second. This includes, for example, LRMand SFF-8431, a specification currently under development by the SFFCommittee that will document the SFP+ specifications for 10G Ethernetand other 10G systems.

Recent developments in 10G optical communications have included the useof Electronic Dispersion Compensation (EDC) in receivers to extendrange. For example, the IEEE 802.3aq standards committee has developed astandard (10 GBASE-LRM or simply LRM) for 10G Ethernet over multi-modefiber over distances of up to 220 meters using EDC. This standard isdocumented in IEEE Std. 802.3aq-2006 (IEEE Standard for Informationtechnology-Telecommunications and information exchange betweensystems-Local and metropolitan area networks-Specific requirements, Part3: Carrier Sense Multiple Access with Collision Detection (CSMA/CD)Access Method and Physical Layer Specifications, Amendment 2: PhysicalLayer and Management Parameters for 10 Gb/s Operation, Type 10GBASE-LRM), referred to herein as IEEE 802.3aq-2006 or LRM, andincorporated by reference.

However, there are many challenges to implementing 10G systems,especially over multi-mode fibers. Multi-mode fibers generally are ahigh dispersion communications channel with a significant amount ofvariability from fiber to fiber, and even within the same fiber over aperiod of time. In addition, one of the first components in a receiveris the analog to digital converter (ADC). However, a 10G system requiresa 10G ADC, which can be difficult and expensive to build with therequired resolution. More generally, various other components in thereceiver may also be difficult or expensive to build at this speed ofoperation. In some instances, high-speed operation can be achieved bymoving to more complex circuit designs or less frequently used materials(e.g., GaAs). However, added complexity often comes at the price ofhigher cost or lower reliability. The use of different materials systemsmay increase the cost by increasing the overall count of integratedcircuits if the materials systems cannot be combined on a singleintegrated circuit.

SUMMARY OF THE INVENTION

The present invention overcomes the limitations of the prior art byproviding a receiver and/or transceiver with various features orcombinations of features. In one aspect, the receiver includes aninterleaved ADC coupled to a multi-channel equalizer that can providedifferent equalization for different ADC channels within the interleavedADC. That is, the multi-channel equalizer can compensate forchannel-dependent impairments. In one approach, the multi-channelequalizer is a feedforward equalizer (FFE) coupled to a Viterbi decoder,for example a sliding block Viterbi decoder (SBVD). In one approach, theFFE and/or the channel estimator for the Viterbi decoder are adaptedusing the LMS algorithm.

In various aspects, the interleaved ADC can include differentcombinations of features. For example, the interleaved ADC can be basedon a lookahead pipelined architecture. It may additionally use open-loopresidue amplifiers in the pipeline, rather than closed-loop amplifiers.The non-linearity of the open-loop amplifiers can be corrected bycalibration, in one approach based on lookup tables. In one design, theADC pipeline units perform an N-bit digital conversion but the ADCpipeline units themselves generate M raw bits, with M>N (i.e., asub-radix architecture), thus adding redundancy to compensate for thelower accuracy open-loop amplifiers. If lookup table calibration isused, the M raw bits can be used as an address to the lookup table. Thecontents at any M-bit address are the corresponding N-bit digitalrepresentation. Optionally, a calibration unit can update the lookuptable, possibly automatically during operation. In one approach, thereare two pipeline units for each ADC channel of data, with one unitperforming A/D conversion while the other unit is being calibrated.

In another aspect, the multi-channel equalizer can also includedifferent combinations of features. In one implementation, themulti-channel equalizer is implemented based on N-tap, M-parallel finiteimpulse response (FIR) filters. Two architectures for the FIR aremultiply-accumulate and lookup table-accumulate (where themultiplication is implemented by lookup table). For FIR filters withlarger numbers of taps, a multi-stage architecture can be used. Forexample, a 25-tap FIR filter can be implemented as 5 groups of 5-tapfilters.

Different levels of “multi-channelness” are also possible. At oneextreme, each equalizer coefficient in the multi-channel equalizer isdedicated exclusively to one and only one of the interleaved channels.In this approach, each of the channels can be adjusted entirelyindependently of the others. At the other extreme of no“multi-channelness,” the same coefficients are applied to allinterleaved channels. In hybrid approaches, at least some of theequalizer coefficients are shared by at least some (and possibly all) ofthe interleaved channels. Alternately, the equalization can include botha term based on shared coefficients and another term based onchannel-dependent coefficients.

In some implementations, the interleaved channels from the ADC are notrecombined into a single high-speed channel before equalization. Rather,the parallelism is maintained and the multi-channel equalization appliedin that format. In fact, the incoming data may be demultiplexed evenfurther if, for example, the equalizer circuitry runs at a slower speedthan the ADC circuitry.

One aspect of the equalizer is its adaptation. In one design, themulti-channel equalizer includes an FFE coupled to a SBVD and LMSadaptation is used for both the FFE and the channel estimator for theSBVD. However, the adaptation can be implemented on a sub-sampled basis.If the parallel format of the interleaved ADC is preserved, then eachADC channel is inherently sub-sampled since one ADC channel alone doesnot contain all samples. Sub-sampled adaptation would be advantageoussince it can avoid the complicated circuitry required by adaptationsbased on all samples.

In another aspect, a timing recovery circuit is used to drive the clockfor the interleaved ADC. In one implementation, the timing recoverycircuit includes a “pulse preprocessor,” which is used to adapt totime-varying impulse responses of the channel, as is common formulti-mode fibers. In addition, the timing recovery circuit can bedriven by the output of the interleaved ADC, rather than the output ofthe multi-channel equalizer, as this reduces the latency in the timingrecovery feedback loop, thus enabling a higher loop bandwidth.

In another aspect, automatic gain control is applied to the incomingsignal. A multi-stage gain control can be used, including coarse andfine gain control, for example.

In one specific implementation, a transceiver chip is designed for 10Gapplications. Using the XAUI interface as an example, the on-chiptransmit path includes the XAUI interface, an encoder/decoder, MUX andpre-driver. The laser driver and laser are provided off-chip.

In the receive path, the photodiode and transimpedance amplifier areprovided off-chip. The chip includes a programmable gain amplifier thatapplies a variable gain to the incoming signal from the transimpedanceamplifier. The gain is controlled by the two-stage coarse and fineautomatic gain control. The output of the programmable gain amplifierenters the interleaved ADC, which in this example includes eight ADCchannels of nominally 1.25GS/s each. Each channel includes two ADCpipeline units (based on lookahead pipeline with sub-radixarchitecture), which automatically switch between active operation andcalibration. The digital data from the eight ADC channels then enter themulti-channel equalizer. They are also used to drive the automatic gaincontrol and the timing recovery circuitry.

In this example, the multi-channel equalizer is actually 16-parallel.Each of the eight ADC channels is further demultiplexed by a factor oftwo so that the equalizer can run at a slower speed. The multi-channelequalizer includes an FFE coupled to a SBVD, both of which are adaptedusing LMS as described above. Much of the basic filter architectures arebased on lookup tables. The output of the multi-channel equalizer isinput to the XAUI interface. The single chip implementation includes allof the functional blocks described above, from XAUI interface topre-driver on the transmit path and from programmable gain amplifier toXAUI interface on the receive path. This particular implementation isgiven as an example. The invention is not limited to thisimplementation, nor is every other design required to have every featuredescribed in this implementation.

Yet another aspect is a startup procedure for the receiver. In oneapproach, the coarse gain control is set, followed by the fine gaincontrol. The timing recovery circuitry can then acquire phase lock withthe incoming signal. The ADC pipeline units typically will alsoauto-calibrate before or during this process. The multi-channelequalizer is then converged. This can be a two-step process, with thefirst step being the selection of cursor delays that minimize the errorsignal and the second step being the convergence of the equalizer giventhe selected cursor delays.

Other aspects of the invention include various combinations of thefeatures described above, devices that use these combinations, systemsbased on these devices and methods related to any of the foregoing.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention has other advantages and features which will be morereadily apparent from the following detailed description of theinvention and the appended claims, when taken in conjunction with theaccompanying drawings, in which:

FIG. 1 is a block diagram of a system including the present invention.

FIG. 2 is a block diagram of one embodiment of the interleaved ADC ofFIG. 1.

FIG. 3 is a block diagram of one embodiment of the DSP of FIG. 1.

FIG. 4 is a block diagram of a pipelined ADC architecture suitable foruse as an ADC channel.

FIG. 5 is a block diagram of an ADC pipeline with lookahead capability.

FIG. 6 is a block diagram of a lookup table approach to compensating forADC non-linearity in an ADC pipeline.

FIGS. 7A-7C are block diagrams modeling the interleaved ADC as part ofthe communications channel.

FIG. 8 is a block diagram of a 4-tap, 8-parallel FIR suitable for usewith the MLSE of FIG. 3.

FIGS. 9A and 9B are block diagrams of different implementations of theprocessing elements used in the parallel FIR of FIG. 8.

FIG. 10 is a block diagram of another FIR structure.

FIG. 11A is a block diagram of one implementation of the LMS adaptationengine of FIG. 3.

FIG. 11B is a block diagram of one implementation of the lookup tablerefresh unit of FIG. 11A.

FIG. 12A is a block diagram of one implementation of the LMS adaptationengine for the channel estimator of FIG. 3.

FIG. 12B is a block diagram of one implementation of the lookup tablerefresh unit of FIG. 12A.

FIGS. 13A-13C and 14 are block diagrams of different implementations ofthe timing recovery circuit of FIG. 3.

FIG. 15 is a block diagram of an implementation of the automatic gaincontrol circuit of FIG. 3.

FIG. 16 is a flow diagram illustrating a start-up sequence for thereceiver of FIG. 1.

FIG. 17 is a block diagram of a transceiver module using the receiver ofFIGS. 1-16.

FIG. 18 is a block diagram of an adaptation of an MLSE, according to oneembodiment.

FIG. 19 is a block diagram illustrating calculation of metric M.

The figures depict embodiments of the present invention for purposes ofillustration only. One skilled in the art will readily recognize fromthe following discussion that alternative embodiments of the structuresand methods illustrated herein may be employed without departing fromthe principles of the invention described herein.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 shows an optical fiber communications link 100 according to theinvention. The link 100 includes a transmitter 105 coupled throughoptical fiber 110 (the communications channel) to a receiver 115. Atypical transmitter 105 may include a serializer or parallel/serialconverter (P/S) 106 for receiving data from a data source on a pluralityof parallel lines and providing serial data to a laser driver 108. Thedriver 108 then drives a laser source 109, for example a 1310 nmFabry-Perot or DFB laser. The laser source 109 launches the opticalwaveform carrying the digital data on optical fiber 110.

On the receive side, a typical receiver 115 includes a photodetector 111for receiving and detecting data from the optical fiber 110. Thedetected data is typically processed through a transimpedance amplifier(TIA) 112. A programmable gain amplifier (PGA) 120 applies a variablegain to the electrical analog signal. The resulting electrical signal isconverted to digital form by an interleaved ADC 130. The interleaved ADC130 is timed by a clock signal produced by the sampling clock generator140. The digital output of the ADC 130 is further processed by digitalsignal processing circuitry (DSP) 150 to recover the digital data. Inthis example, the DSP implements electronic dispersion compensationusing a multi-channel equalizer. The recovered data may then be placedon the appropriate interface by interface circuitry 116. For example, ifreceiver 115 is implemented on a chip that is mounted on a host, theinterface 116 may be an interface to the host. In FIG. 1, the interfaceis shown as a 10G data stream with n parallel lines. The DSP 150 alsoproduces signals for the sampling clock generator 140 and the PGA 120.

The following example will be illustrated using a 10G receiver. While10G systems serve as convenient examples for the current invention, thecurrent invention is not limited to 10G systems. Examples of othersystems to which the current invention could be applied include FibreChannel systems, which currently operate at speeds from 1 Gbps to 10Gbps, as specified by the Technical Committee T11, a committee of theInterNational Committee for Information Technology Standards (INCITS).

One place where the following examples deviate from the LRM standard isthe fiber length. The draft standard specifies 220 meters, but thefollowing examples use a 300 meter length. This is motivated by thelarge number of fibers in the field whose length approaches 300 meters,and by the fact that users of EDC technology have expressed a desire forthis extended reach. Although the LRM channel is used in the followingexamples in order to make them more concrete, the techniques illustratedare general and they can be used in many other fiber optic or othercommunications applications. Other fiber optic applications for whichthese techniques can be used include, for example, systems using singlemode optical fiber as the communications medium.

FIGS. 2 and 3 provide more detailed block diagrams of receiver 115. Thefigures after that provide even more detail on the operation of specificcomponents shown in FIGS. 2 and 3.

FIG. 2 is a block diagram of one embodiment of the interleaved ADC 130.The interleaved ADC contains M parallel ADC channels 230A-230M. Each ADCchannel 230 typically includes a track-and-hold (T&H) unit 234 followedby the actual ADC conversion circuitry 236. The ADC channels 230A-H aretime interleaved by an analog demultiplexer 231 and digital retimer 237.In this example, the analog demultiplexer 231 demultiplexes the incoming10G analog signal into eight parallel time-interleaved ADC channels 230.Each ADC channel 230 operates at a nominal conversion rate of 1.25 GS/s(actual conversion rate 1.29 GS/s). In addition, a multi-channelequalizer 350 in the DSP (see FIG. 3) corrects channel-dependentimpairments from the interleaved ADC, as well as other impairments suchas dispersion introduced by the optical fiber 110.

In some embodiments, the retimer 237 may also multiplex the eight ADCchannels 230 back into one or more higher data rate signals (e.g., intoone 10G signal, or two parallel 5G signals, etc.). In the particularimplementation shown in FIG. 2, the DSP 150 uses a parallelizationfactor of 16, as will be described below, so the retimer 237 not onlyretimes the ADC channels 230 but also demultiplexes each ADC channel 230by a factor of two.

FIG. 3 is a block diagram of one embodiment of the DSP 150. The DSP 150can roughly be divided into three sections: automatic gain control (AGC)circuitry 320, timing recovery circuitry 340 and the rest of the DSP150, which implements a multi-channel equalizer 350. The AGC circuitry320 receives the ADC output and generates a control signal for the PGA120. The timing recovery circuitry 340 provides signals to the clockgenerator 140 in order to produce a clock for the ADC 130 that isaligned with the incoming analog signals. Note that the timing recoverycircuitry 340 takes the ADC output as its input (rather than themulti-channel equalizer 350 output as its input). This reduces thelatency in the timing recovery feedback loop, thus enabling a higherloop bandwidth without making the feedback loop unstable.

The multi-channel equalizer 350 in this example is a maximum likelihoodsequence estimation (MLSE) equalizer. This is motivated by the fact thatthe optimal receiver for an intersymbol interference channel in thepresence of Gaussian noise consists of a whitened matched filterfollowed by a maximum likelihood sequence detector. The equalizer 350includes a MIMO-FFE (C) 360 coupled to a sliding block Viterbi decoder(SBVD) 370 and a MIMO channel estimator (B) 380. This architecture isable to compensate for the ISI of MMF, as well as for the impairments ofthe receiver front-end, such as channel-to-channel variations in theinterleaved ADC 130.

In more detail, the MIMO-FFE 360 applies feed-forward equalization tothe digital data received from the ADC 130. The coefficients for theequalization are updated using the LMS algorithm, as implemented bycircuitry 362. The SBVD 370 then makes decisions based on the equalizedsamples from the FFE 360. These are output as the digital data recoveredby the receiver (or possibly converted from serial to parallel form).Circuitry 380 is the channel estimator for the SBVD 370. The estimatedchannel is used by the SBVD 370 to make its decisions. An errorcomputation unit 381 calculates the error between the FFE 360 outputq_(n) and the output of the channel estimator 380. The error signalproduced by the channel estimator 380 is used by the LMS updatecircuitry 362 to update the coefficients for the FFE 360 and is alsoused by the timing recovery circuitry 340 to adjust the clock 140driving the ADC 130. The channel estimator 380 itself is also adaptive,in this example also based on the LMS algorithm.

FIGS. 4-6 provide further details on one implementation of the ADC 130of FIG. 2. FIG. 4 is a block diagram illustrating a pipelined ADCarchitecture suitable for use as an ADC channel 230. The ADC pipeline230 includes an input track-and-hold stage 234 followed by a number oflow resolution ADC stages 420A-420N. The stages 420 preferably areidentical, except that the beginning and ending stages may be differentdue to their location at the beginning or end of the pipeline 230. Inthis example, each ADC stage 420 is a 1-bit stage. Each stage 420includes a 1-bit analog-to-digital converter (e.g., a comparator) 421, a1-bit digital-to-analog converter 422 (e.g., a switch), an analogsubtractor 423, a gain stage (i.e., the residue amplifier) 425, and atrack-and-hold circuit 429. The 1-bit ADC 422, which will also bereferred to as a sub-ADC, makes a 1-bit decision on the input signalV_(ini) for the stage 420. This bit d_(i) is used in the sub-DAC 422 togenerate a voltage V_(DASCi) representing the contribution of that bitd_(i) to the input signal V_(ini). The subtractor 423 subtracts thecontribution V_(DASCi) from the input signal V_(ini) to develop aresidue, which is the remaining value of the input signal after thevalue of the previously decided bits is removed. The residue amplifier425 multiplies the residue by a gain value G (which is 2 if the stageconverts one effective bit). The resulting residue res_(i) is held in atrack-and-hold circuit 429 and used as the input signal V_(ini) for thenext stage. Thus, each stage is operating to produce 1 bit of theresult. The gain of 2 applied by the residue amplifier 425 scales theresidue so that the same circuitry can be used for the next stage. Thespeed of this converter is limited by the critical path consisting ofthe 1-bit ADC (typically a comparator) 421, the 1-bit DAC (which istypically just a switch) 422, the subtractor 423, and the residueamplifier 425.

In one implementation, unlike conventional ADC pipelines, the residueamplifiers 425 are implemented as open-loop amplifiers rather thanclosed-loop amplifiers. Closed-loop amplifiers can be more closelycontrolled, in terms of parameters such as gain and nonlinearity.However, closed-loop amplifiers have more severe speed limitations orrequire more power to achieve a given speed than open-loop amplifiers.The use of open-loop amplifiers provides higher speed (increases swingand bandwidth) with lower power. It can also reduce requirements ontransistor performance.

However, because the gain G provided by open-loop amplifiers 425 can beless controlled, some form of redundancy is preferably employed to avoidthe loss of analog information in the pipeline. In one approach, asub-radix architecture with redundancy is used. In a non-redundantarchitecture, the total number of raw bits d_(i) generated by the stages420 is the same as the number of bits in the digital representation. Ina redundant architecture, the stages 420 produce more raw bits d_(i)than the number of output bits in the digital representation. The extrabits represent redundant information which is used to correct errors inthe pipeline. In a sub-radix architecture, each stage 420 outputs oneraw bit d_(i) but effectively converts less than one output bit of thedigital representation. Therefore, the total number of stages 420 ismore than the number of output bits in the digital value.

For example, in one non-redundant architecture, each stage 420effectively converts 1 bit and the residue amplifier gain G is 2.Therefore, eight stages 420 are required to implement an 8-bit A/Dconversion. The eight raw bits d_(i) are the actual output bits in thedigital representation of the analog value, with the raw bit from stage1 being the most significant output bit. As an example of a sub-radixarchitecture, each stage 420 might generate 1 raw bit but convert only0.8 output bits with a residue amplifier gain G of 2^(0.8). More stages420 are required, 10 stages in this case to implement an 8-bit A/Dconversion. The 10 raw bits d_(i) from the stages 420 are not the 8output bits in the digital representation but are used to generate thefinal 8 bits using known algorithms. The sub-radix architecture allowsgains errors to be tolerated by an amount proportional to the amount ofgain reduction. It also allows redundancy with not much additionalhardware.

A popular redundancy technique is a 1.5 output bits/stage architecture.In this technique, each stage 420 outputs 2 raw bits (thereby requiringadditional comparators, which dissipate additional power), and backendprocessing uses this redundant information to improve accuracy. Usingthis technique, the accuracy of the ADC pipeline is set primarily by theaccuracy of the interstage gain G. Because the gain of open-loopinterstage amplifiers 425 is not as well controlled, this technique isnot preferred for the present application. A sub-radix architecture, onthe other hand, maintains 1 output bit per stage but provides redundancyby interstage gains of less than 2, and the accuracy of the interstagegain G is not as central to the architecture. This requires additionalstages 420 (for example, an 8-bit ADC pipeline might require 10 or 11stages using this technique) but only 1 comparator per stage. Again,backend processing uses the redundant information to provide therequired accuracy.

FIG. 5 is a block diagram of an ADC pipeline with lookahead capability.In a conventional ADC pipeline, the high speed comparator 421regenerates between clock phases. This allows the comparator output timefor positive feedback to drive the output to the desired value. Becauseof the relatively slower closed-loop interstage amplifiers, the clockperiod is set long enough that the comparator 421 has plenty of time toregenerate. However, with faster open-loop interstage amplifiers 425 andthe resulting shorter clock periods, the comparator 421 may not haveenough time to completely regenerate. One solution is to use a lookaheadpipeline.

In the lookahead pipeline, the critical timing path, consisting of theamplifier settling time plus the comparator regeneration time, is brokeninto two shorter paths. In the example shown, all stages 420 (other thanthe first stage 420Q) have a pair of comparators 421(X) and 421(Y)(rather than a single comparator) that operates to develop the possiblevalues for the stage based on the input value to the previous stage.This basically allows the interstage amplification and the comparatoroperation to occur in parallel, giving the comparators an entire clockhalf-period to regenerate. In this architecture, the first stage 420Q(that generates raw bit D₁) is a “half-stage” that uses a singlecomparator. The remaining stages 420B-N use two comparators 421 perstage. The last stage may be simplified since there is no followingstage. The last stage could contain only the circuitry required togenerate the last raw bit D_(N) (e.g., eliminating the subtractor 423Nand open-loop amplifier 425N). The architecture is somewhat more complexthat an ADC pipeline without lookahead, but it allows much higher speedswhen the interstage amplifier's speed is comparable to the comparator'sspeed.

In some sense, the sub-ADC 421 operation for a lookahead stage is movedahead one stage. Referring to FIG. 5, stage 420B determines bit D₂.However, the input value to stage 420B is the original V_(in). It is notthe residue of V_(in) after the contribution due to bit D₁ has beenremoved, as would be the case in an ADC pipeline without lookahead. Infact, the output of stage 420B (rather than the input) is the residueafter the D₁ contribution has been removed. This one-stage shift is whatallows the interstage amplification and the comparator operation tooccur in parallel.

However, the sub-ADC 421 for stages 420B-N becomes more complex. Thesub-ADC 421B for the second lookahead stage 420B includes twocomparators 421B(X) and 421B(Y). These comparators determine the bit D₂for stage 420B. Comparator 421B(X) determines bit D₂ assuming that bitD₁ is a 1. Comparator 421B(Y) determines bit D₂ assuming that bit D₁ isa 0. Switch 427B determines which result to select, depending on theoutput of sub-ADC 421Q of the previous stage 420Q. The bit D₂ is fed tothe sub-DAC 422C of stage 420C.

As described above, the lookahead pipeline architecture allows a fullclock half period for the comparators to regenerate. There is also thepotential to use part of the amplifier settling time for comparatorregeneration, since the amplifier output will be approaching its finalvalue closely enough that the comparator threshold has been passed andthe comparator can begin regenerating.

FIG. 6 is a block diagram illustrating a lookup table approach tocompensating for amplifier non-linearity. A pipelined ADC typicallyrequires fairly linear residue amplifiers 425 if the result is to beused without additional correction. One drawback of using open-loopamplifiers 425 is they can be non-linear. Different approaches can beused to compensate for effects caused by the non-linearity of open-loopamplifiers 425. FIG. 6 illustrates one approach using a lookup table.

Each interleaved ADC channel 230 includes two pipeline units 610(1) and610(2). Each ADC pipeline unit 610 includes an ADC pipeline 630 followedby a calibration unit, which in this example is a lookup table 640. As aresult of the non-linearities of the individual stages 420 in thepipeline 630, the response of the overall ADC pipeline 630 has a complexnon-linear characteristic, denoted in FIG. 6 by a function f(vin). Inother words, the raw bits d_(i) generated by the pipeline stages do notmap in a linear manner to the output bits in the final digitalrepresentation. In FIG. 6, the “distorted” raw bits d_(i) from the ADCpipeline 630 are applied to a lookup table 640 which stores the inverseof the non-linear characteristic. Thus, the LUT 640 reverses the effectsof the non-linear open-loop amplifiers, and the output of the LUT 640 isused as the digital output of the ADC.

Each ADC channel 230 includes two pipeline units 610(1) and 610(2) whichare constantly being swapped between normal operation and calibrationmodes, at a rate of about 1 MHz. At any given instant, one of the twopipelined units is in normal operation, while the other is incalibration. Approximately every microsecond, the units areautomatically interchanged. Therefore, to an external observer, the pairof pipelined units 610(1) and 610(2) operates as a single high-precisionADC channel 230.

For the pipelined unit 610(1) that is in normal operation, thecalibration portion of a pipelined unit 610(1) behaves as a simplelookup table 640(1). The raw output from the ADC pipeline 610(1) is thememory address used to access the lookup table 640(1). The content atthis memory address is the digital output of the ADC channel 230.

For the pipelined unit 610(2) that is in calibration, the lookup table640(2) contents are updated. The update is based on a reference rampgenerated by a digital counter 615 followed by a high precision DAC 617,which provides the input for the ADC pipeline 610(2) under calibration.Since the ramp can be relatively slow, a digital ramp can be generatedfrom the DSP 150. The lookup table 640(2) is updated using an LMSalgorithm, where the error is computed as the difference between thecurrent content of the lookup table entry addressed by the pipelineoutput and the expected output, which is the output of the counter 615.If the two quantities are identical, the lookup table 640(2) entry isalready correct and it does not need to be updated. Correspondingly, theerror is zero, so that no update takes place. However, if the twoquantities differ, there will be an update. The LMS algorithmeffectively averages many updates, so that the entries in the lookuptable 640(2) are not computed based on a single conversion, but on anaverage of many conversions.

Now consider the design of an interleaved ADC for the following 10Gexample:

-   -   10 GS/s nominal conversion rate (10.3125 GS/s actual conversion        rate)    -   8 bit accuracy

In one design, the ADC includes eight parallel time-interleaved ADCchannels 230A-H. Each ADC channel 230 operates at a nominal conversionrate of 1.25 GS/s (actual conversion rate 1.29 GS/s). Each ADC channel230 includes two ADC lookahead pipelines 630 of 11 stages each, with onepipeline in service at any one time and the other available forcalibration. Each of the 16 lookahead pipelines 630 uses open-loopinterstage amplifiers and subranging lookahead pipeline architecture.Lookup table calibration compensates for non-linearities. There are 16lookup tables for the non-linear calibration, one for each of the 16pipelines. Each lookup table takes the 11-bit raw input from thelookahead pipeline as input and outputs the corrected 8-bit digitalvalue.

Allowing for the expected worst case offset values and interstage gaintolerance (for the open-loop amplifiers), computing the requiredredundancy gives an ADC pipeline with 11 stages and an interstagenominal gain G of 1.75. The 3 sigma input referred offset includingcomparators and residue amplifiers is estimated at 26 mV. This resultsin an interstage gain G of less than 1.82. With gain G=1.75, 11 stagesare required to achieve 8 bit performance with 10% tolerance on the gainG.

The digital output of the interleaved ADC 130 is further processed bythe multi-channel equalizer 350. FIGS. 7-12 provide further details onone example of a suitable multi-channel equalizer 350.

FIG. 7A shows a model of the communications link where the impairmentsof the analog front end, particularly the M-parallel time interleavedADC system 130, are explicitly shown as part of the communicationschannel. Here, h(t) models the optical channel 110 response as well asthe receive filter and any other linear element present in the link anda_(k)ε{−1, +1} are the transmitted symbols. Blocks f₀(t) to f_(M-1)(t)model the frequency responses of each track-and-hold (T&H) unit 234 inthe interleaved ADC 130. This response can vary from one ADC channel 230to the next. Gain errors and offsets in the ADC channels 230 are modeledby g₀ to g_(M-1) and O₀ to O_(M-1), respectively. Finally, δ₀ to δ_(M-1)model sampling time errors. The output of the system is comprised by Mparallel samples, r_(n) ⁽⁰⁾ to r_(n) ^((M-1)), which will be processedby the multi-channel equalizer 350. Note that the superscript identifiesbaud spaced samples, whereas subscript n represents samples spacedM-baud periods apart. For simplicity, noise sources such as additivewhite Gaussian noise (AWGN) and quantization noise are not explicitlyshown in the diagram.

First transform filters h(t) and f₀(t) through f_(M-1)(t) from thecontinuous to the sampled time domain. The transformation assumes idealsampling (sampling without phase errors). Sampling time errors will bemodeled with a multiple-input, multiple-output (MIMO) interpolationfilter, as will be seen later. Defining:

a _(n) ^((i)) =a _((nM−i)) i=0, . . . M−1,  (1)

a MIMO description of this communications link is obtained by convertingthe single-input, single-output (SISO) filters h(t) and f₀(t) throughf_(M-1)(t) to a MIMO and a multiple-input, single-output (MISO)representation, respectively, as shown in FIG. 7B. The MIMO and MISOmodels can be combined to obtain a single MIMO representation.

In this way, the MIMO model accepts M-dimensional input vectors whosecomponents are transmitted symbols, and produces M-dimensional outputvectors whose components are signal samples, at a rate 1/MT. FIG. 7Cshows a diagram of the MIMO model. The vector of input symbols a_(n)feeds the communications channel response matrix H(z). The output ofthis channel is fed to the T&H matrix filter F(z), which models theindependent T&H responses. P(z) models the sampling time errors. It canbe seen as a block that interpolates the samples taken without samplingerrors at the output of the channel and generates M outputs withsampling errors. With identical T&H responses, the sampling time errorscan be modeled using an interpolation filter that generates samples withphase errors for each output of the MIMO model. When T&H responses aretaken into account, a possible way to continue to use the interpolatorfilter is to invert the response of F(z), as is shown inside the dottedline of FIG. 7C. While the use of an interpolation filter is completelyaccurate only when samples are free of aliasing, it can still be used asan approximation when there is some aliasing owing to T-spaced samplingand excess bandwidth greater than zero. This approximation is valid whensampling time errors are small. Finally, matrix G and vector O representgain and offsets errors, respectively. From FIG. 7C, the MIMO model canbe written as:

r(z)=GP(z)F(z)H(z)a(z)+O(z).  (2)

Grouping the factors in the first term of the sum as S(z)=GP(z)F(z)H(z),the entire MIMO response of the system can be represented in thez-domain and time-domain, respectively, as:

$\begin{matrix}{{r(z)} = {{{S(z)}{a(z)}} + {O(z)}}} & \left( {3A} \right) \\{r_{n} = {{\sum\limits_{l = {- \infty}}^{\infty}{S_{l}a_{n - 1}}} + {O.}}} & \left( {3B} \right)\end{matrix}$

Given the model of Eqn. (3), the joint compensation of the channelimpairments (such as intersymbol interference (ISI)) and the analogfront-end (AFE) errors can be formulated as the general equalizationproblem of a MIMO channel. Common equalization techniques include feedforward equalization, decision feedback equalization, and maximumlikelihood sequence estimation.

FIG. 3 illustrates an example using maximum likelihood sequenceestimation (MLSE). FIG. 3 includes a block diagram of an MLSE equalizer.The equalizer 350 includes a MIMO-FFE (C) 360 coupled to a sliding blockViterbi decoder (SBVD) 370 and a MIMO channel estimator (B) 380.

In one implementation, the MIMO-FFE 360 is described by the followingequation:

$\begin{matrix}{q_{n} = {\sum\limits_{l = 0}^{N_{f} - 1}{C_{l}r_{n - 1}}}} & (4)\end{matrix}$

where N_(f) is the number of M×M-matrix taps (C_(i)) of the forwardequalizer.

Let K be the total number of bits transmitted. It is convenient toassume, without loss of generality, that K=NM with N integer. Themaximum-likelihood sequence detector chooses, among the 2^(K) possiblesequences, the one {â_(k)} (κ=1, . . . , K) that minimizes the metric:

$\begin{matrix}{{m = {\sum\limits_{n = 1}^{N}{{q_{n} - {B\left( {\hat{A}}_{n} \right)}}}^{2}}},} & (5)\end{matrix}$

where B(•) is a function that models the response of the equalizedchannel with memory Δ−1, and Â_(n)−(â_(nM), â_(nM−1), . . . ,â_((n−1)M−Δ+2)). Note that each component of B(•) depends only on Δconsecutive received bits. This formulation assumes that in general thefunction B(•) is nonlinear. The minimization of Eqn. (5) can beefficiently implemented using the Viterbi algorithm. The required numberof states of the Viterbi decoder is S=2^(Δ−1). The SBVD 370 is generallya suitable form of the Viterbi algorithm for a MIMO receiver. The inputto the SBVD 370 is the FFE 360 output vector q_(n), and the output is ablock of M detected symbols â_(n).

For each of the M components of B(Â_(n)), the MIMO channel estimator 380generates the 2S expected values of the corresponding component of theq_(n) vector for all possible combinations of the Δ most recentlyreceived bits (corresponding to the 2S branch metrics in the trellisdiagram). The MIMO channel estimator 380 can be implemented using Mlookup tables, each lookup table having 2S entries. While the vectorB(Â_(n)) can in general take on 2^(M)S values, dynamic programmingtechniques inherent in the Viterbi algorithm reduce the computationalrequirement to that of computing the 2MS branch metrics corresponding tothe individual components of B(Â_(n)).

The coefficients of the FFE 360 and the lookup tables can be iterativelyadapted using the well known LMS algorithm, as follows for iteration j:

e _(n) =B ^(j)(Â _(n))−q _(n),  (6)

C ₁ ^((j+1)) =C ₁ ^((j)) +βe _(n) r _(n−1) ^((T)),  (7)

B ^(j+1)(Â _(n))=B ^(j)(Â _(n))−γe _(n)  (8)

where (•)^(T) means transpose and β and γ are the algorithm step sizesof the FFE and channel estimator, respectively. The iteration number jof the LMS update is shown as a superscript. The LMS update circuitry362 carries out this function.

Note that the absence of a reference level in Eqns. (6)-(8) definescoefficients of the FFE 360 and the channel estimator 380 only up to ascale factor. One possible way to define the scale is to set one of thecoefficients of the FFE 360 to a specific value which is kept fixed (notadapted). In the 10G example, the number of taps of the FFE 360 can beprogrammed by the user. This allows the user to trade performance forpower consumption. For similar reasons, the number of states of theViterbi decoder 370 can also be set by the user.

The parallel implementation of the FFE 360 is closely related to theMIMO structure. From the MIMO representation, the FFE 360 can beexpanded as a convolution matrix as follows:

$\begin{matrix}{{C = \begin{bmatrix}c_{0}^{(0)} & c_{1}^{(0)} & \ldots & c_{L_{f} - 1}^{(0)} & 0 & \ldots & 0 \\0 & c_{0}^{(1)} & c_{1}^{(1)} & \ldots & c_{L_{f} - 1}^{(1)} & \ldots & 0 \\0 & 0 & \; & \ldots & \; & \; & 0 \\0 & 0 & \ldots & c_{0}^{({M - 1})} & c_{1}^{({M - 1})} & \ldots & c_{L_{f} - 1}^{({M - 1})}\end{bmatrix}},} & (9)\end{matrix}$

where L_(f) is the number of taps used. Then the output samples arecomputed as:

q _(n) =C[r _((nM)) r _((nM−1)) . . . r _(((n−1)M+Lf−1))]^(T)  (10)

The parallel implementation of the FFE 360 can be represented by M FIRfilters, which is precisely what Eqn. (10) represents. In the presenceof mismatches in the AFE, the coefficients in different rows of Eqn. (9)are different. This effectively allows different equalization to beapplied to each of the interleaved channels (although the equalizationcan be applied after the interleaved channels have been recombined). TheMIMO structure of the Viterbi decoder 370 is also essentially identicalto the parallel processing realization. The only modification is thatbranch metrics associated with different components of the input vectorq_(n) are computed using different components of the channel estimatorfunction B, which is not the case in a traditional parallelimplementation. Although in Eqns. (9) and (10) the implicit assumptionis made that the DSP parallelization factor equals the dimension of theMIMO channel, in practice this constraint is not required.

FIGS. 8 and 9 illustrate example implementations of parallel FIR filterssuitable for implementing Eqn. (9). FIG. 8 is a block diagram of a4-tap, 8-parallel FIR. That is, this FIR implements L_(f)=4 and M=8 inEqn. (9). The x_(n) are the input data and the y_(n) are the filtered,output data. Note that x_(n) and x_(n−1) represent data that are spaced1-baud period apart but consecutive samples of x_(n) represent data thatare spaced M-baud periods apart. The blocks marked T are delay lines,each of which represents a delay of M-baud periods. The blocks marked Fare processing elements, each of which implements the multiply andaccumulate represented by one row of the matrix in Eqn. (9).

FIGS. 9A and 9B show two different implementations of the processingelement F. FIG. 9A is based on a multiply-accumulate architecture. FIG.9B is based on a lookup table-accumulate architecture. In both of thesefigures, the inputs x_(n) are represented by a delay line structure,where each delay t is 1-baud period. This is merely a symbol indicatingthe relative timing of the inputs x_(n), the actual implementation ofthe FIR may or may not have this specific delay line structure. In FIG.9A, multipliers 910 are used to multiply the inputs xn by the tapweights cn to produce intermediate products. Adders 920 then sum theintermediate products to produce the result y. In FIG. 9B, themultipliers 910 are replaced by lookup tables 930 that output theintermediate product, but by a lookup table operation rather than by amultiplication operation.

In the 10G example, a 25-tap, 16-parallel FIR is used. Recall that theincoming 10G signal is decimated into 8 1.25G signals but that the ADCchannel processing each of these signals uses two ADC pipelines, one isin operation while the other is in calibration. Therefore, there areeight ADC pipelines active at any given time. Each of the eight ADCchannels is demultiplexed by a factor two by the retimer 237 to allow aparallelization factor of 16 in the DSP 150. This is done to reduce theclock rate of the DSP 150. Different parallelization factors can be usedin alternate embodiments. In this example, because there are only 8independent ADC channels, the number of independent equalizers need onlybe 8, not 16. Therefore, each set of coefficients of the equalizer isshared by two channels of the MIMO equalizer.

The basic architecture shown in FIG. 8 is used to implement this FIR.There will be 16 inputs xn and 16 outputs yn (rather than the 8 shown inFIG. 8), and 25 taps will feed each of the processing elements F (ratherthan the 4 shown in FIG. 8). Due to the large number of taps, thetwo-stage FIR structure shown in FIG. 10 is used. The 25 taps aredivided into 5 groups 1010 of 5 taps each. Each group of 5 taps isimplemented using a structure similar to that shown in FIG. 9B. Theoutputs of the 5 groups are then summed to produce the output yn. Oneadvantage of this approach is that if less than 25 taps are used, someof the groups can be powered down in order to save energy.

In the 10G example, the SBVD 370 can be user programmed for either 4states or 8 states. The channel estimator 380 is implemented using a16-term Volterra series expansion and therefore uses either 8 terms or16 terms, depending on the number of states for the SBVD 370. Thecoefficient of the linear term corresponding to the most recentlyreceived bit is forced to 1 to fix the scaling factor for the channelestimator 380. In this implementation, the constant term is forced to 0to avoid competition with other modules that remove baseline wander fromthe signal. In another embodiment, the “constant” term is actuallyadapted, therefore performing baseline wander compensation without theneed for other baseline wander compensation modules. Therefore, thenumber of adaptive terms is 6 for a 4-state decoder and 14 for an8-state decoder. Both the channel estimator 380 and the SBVD 370 aremulti-channel in the sense that, similar to the FFE 360, they areparallelized to support separate equalization of each of the 8 ADCpipelines. Taken to the extreme, there effectively are independent partsof the channel estimator 380 and SBVD 370 for each of the 8 ADCpipelines.

In another aspect, the FFE 360 and channel estimator 380 can be adaptedon a sub-sampled basis. Let R be the parallelization factor of theinterleaved ADC 130 and M be the parallelization factor of the DSP 150.M may be different from R. In the 10G examples, the baseline values areR=8 and M=16.

Referring to Eqns. (6)-(8) above, the LMS update algorithm for the FFE360 can be written as

c(n+1,k)=c(n,k)−βe(n)x(n−k)  (11)

where k is an index that identifies the equalized coefficients, nrepresents time, e is the slicer error, and x is the input signal. Let

n=mM+p with (0≦p<M)  (12)

Then the update algorithm Eqn. (11) can be written

c(m+1,p,k)=c(m,p,k)−βe(m,p)x(mM+p−k)  (13)

If the same coefficients are used to equalize all ADC channels (i.e., ifmulti-channel equalizer is not used), then the dependence of thecoefficients on p can be dropped. The update term preferably should besummed over all ADC channels to average out the effect of sampling phaseerrors. In this case, Eqn. (13) reduces to

$\begin{matrix}{{c\left( {{m + 1},k} \right)} = {{c\left( {m,k} \right)} - {\beta {\sum\limits_{p = 0}^{M - 1}{{\left( {m,p} \right)}{x\left( {{mM} + p - k} \right)}}}}}} & (14)\end{matrix}$

If the coefficients used to equalize different ADC channels are allindependent, then update Eqn. (13) could be used. However, the speed ofupdate can be improved by adding an update component similar to the onecomputed for the case of common coefficients, for example

$\begin{matrix}{{c\left( {{m + 1},p,k} \right)} = {{c\left( {m,p,k} \right)} - {\beta {\sum\limits_{q = 0}^{M - 1}{{\left( {m,p} \right)}{x\left( {{mM} + q - k} \right)}}}} - {\gamma \; {\left( {m,p} \right)}{x\left( {{mM} + p - k} \right)}}}} & (15)\end{matrix}$

In this approach, the channel-dependent update is broken into two terms:one that represents an “average” update for all channels (the β term)and one that represents each channel's deviation from the average update(the γ term). For γ=0, Eqn. (15) reduces to the case of commoncoefficients Eqn. (14). For)6=0, it reduces to the case of entirelyindependent coefficients Eqn. (13).

However, note that update Eqn. (15) is not subsampled. The values of theerror at all times n=mM+p are used to update the coefficients. Theimplementation of this approach would require relatively complexparallel processing. To reduce complexity and power dissipation, it isdesirable to subsample the adaptation, in other words, to adapt thecoefficients without using all samples of the error. Note thatsubsampling may be different for the β and γ terms of the updateequation.

Let the subsampling factors for the β and γ terms of Eqn. (15) beM_(c)=rM and M_(d)=sM, respectively, where r and s are integers greaterthan or equal to 1. This means that both M_(c) and M_(d) are greaterthan or equal to M, which avoids the need for parallel processing.Typically, r and s will be powers of 2. Now let z be the least commonmultiple of r and s. The time index n can then be written as

n=izM+w where (0≦w<zM)  (16)

Substituting this into Eqn. (15) yields the subsampled update algorithm

$\begin{matrix}{{c\left\lbrack {{\left( { + 1} \right){zM}},p,k} \right\rbrack} = {{c\left( {{\; {zM}},p,k} \right)} - {\beta {\sum\limits_{q = 0}^{{{zM}/r} - 1}{{\left( {{{\; {zM}} + {qr}},q} \right)}{x\left\lbrack {{\left( {{\; {zM}} + {qr}} \right)M} + q - k} \right\rbrack}}}} - {{{\gamma }\left( {{{\; {zM}} + {p\; s}},p} \right)}{x\left\lbrack {{\left( {{\; {zM}} + {p\; s}} \right)M} + p - k} \right\rbrack}}}} & (17)\end{matrix}$

As an example, consider the case of M_(c)=64, M_(d)=64, M=16, r=4, s=4and z=4. In this case, coefficients applied to different ADC channelscan be different. Although the coefficients are updated every 1024cycles of the baud clock, the subsampling factor of the common updateterm is only 64, because each update incorporates the contributions of16 error samples. The subsampling factor of the independent terms is64×16=1024. The processor that computes the common updates runs at ¼ ofthe clock rate of the DSP. The processor that computes the independentupdates is shared by all interleaves.

FIGS. 11A-B show block diagrams of an LMS adaptation engine 362 that canimplement Eqn. (17). In FIG. 11A, block 1110 calculates the commonupdate term (the β term in Eqn. (17)) and block 1120 calculates theindependent update terms (the γ term in Eqn. (17)). FIG. 11B is a blockdiagram of one implementation of the LUT refresh unit 1130, suitable foruse with the two-stage FIR structure shown in FIG. 10. In FIG. 10, the25-tap FFE is divided into 5 groups 1010 of 5 taps each. As described inFIG. 9B, these 5-tap filters 1010 can be implemented using lookup tables930. The LUT refresh unit 1130 updates these lookup tables using the LMSalgorithm described above. For simplicity, FIG. 11B shows a refresh unitthat would update lookup tables for 3 groups of 5 taps each (rather thanthe 5 groups shown in FIG. 10).

The channel estimator 380 can be updated in a similar fashion. In the10G example, as described above, the channel estimator 380 isimplemented using a 16-term Volterra series expansion. The constant termis forced to zero to avoid competition with other circuitry thatcompensates for baseline wander. The coefficient of the linear termcorresponding to the most recently received bit is forced to 1 to fixthe scaling factor for the channel estimator 380. Alternatively, thecoefficient of the oldest bit could be set to 1, to force the channelestimator 380 to train to an anticausal response, which may beadvantageous for some channels.

The adaptation algorithm described above for the FFE 360 is also usedfor the channel estimator 380. Eqn. (17) can be used for the channelestimator 380, except that the sign of the two terms involving the erroris plus, and the signal is replaced by decisions and products ofdecisions corresponding to the terms of the Volterra series expansion.FIGS. 12A-B show block diagrams of an LMS adaption engine for thechannel estimator 380. The basic architecture is similar to that of theFFE adaption engine shown in FIG. 11, with the following minormodifications. The signal inputs to the multipliers are replaced bysingle-bit decisions or products from the Volterra series expansion andthe signal buffer in FIG. 11A is replaced by a “decision buffer” whoseinput comes from the SBVD 370. Block 1210 calculates the update term.The LUT refresh unit 1230 can be implemented as shown in FIG. 12B, whichis similar to the architecture of LUT refresh unit 1130. In theembodiment shown in FIG. 12B, note that the first coefficient of thelinear term corresponding to the most recently received bit is forced to1.

The above examples were based on MLSE and LMS, but other techniques canalso be used. Multi-channel equalizers other than MLSE can also be used.Other common equalization techniques include feed forward equalizationand decision feedback equalization.

As an example of one variation, the equalizer 350 in FIG. 3 may beadapted for use with single mode fiber rather than multimode fiber. Insingle mode fibers (SMF), noise is typically non-Gaussian and signaldependent due to the presence of the amplified spontaneous emission(ASE) noise and the photodetector. As a result of this characteristic,the performance of a MLSE which has been designed for use with multimodefibers (i.e., Gaussian channels) will degrade when used with single modefibers. The degradation is more severe in nondispersive channels.However, the MLSE performance can be improved by adapting it for usewith single mode fibers.

Mathematically, let a_(n) be the transmit bit at time instant n. Thesignal at the DSP input is given by

y _(n) =s _(n) +r _(n) +z _(n)  (18)

where s_(n)=f(a_(n), a_(n−1), . . . a_(n−1+d)) is the noise-free signalcomponent, r_(n) is the ASE noise in the electrical domain, and z_(n) isthe thermal Gaussian noise. Note that s_(n) is a nonlinear function of dconsecutive bits. An MLSE detector approach chooses the sequence thatminimizes the cumulative metric defined by

$\begin{matrix}{M = {\sum\limits_{n}{\frac{1}{2\zeta_{s_{n}}}\left\lbrack {{T_{s_{n}}\left( y_{n} \right)} - {T_{s_{n}}\left( s_{n} \right)}} \right\rbrack}^{2}}} & (19)\end{matrix}$

where T_(s)(•) is a given signal-dependent nonlinear transformation, andζ_(s) is the conditional second-order central moment of the randomvariable T_(s)(y_(n)). In SMF channels with ASE and Gaussian noise,T_(s)(y) is well approximated by y^(vs) with 0<v_(s)≦1 and y≧0. Forexample, v_(s)=0.5 for all s for ASE noise, and v_(s)=1 for all s forGaussian noise. For combined noise, typically 0.4≦v_(s)<1 and v_(s),typically is different for each noise-free level of s. In the presenceof combined ASE and Gaussian noise (i.e., a practical situation), theimplementation of the exact signal-dependent nonlinear transformationT_(s)(•) in the receiver architecture can be complex. It is advantageousto use simpler approximations in order to reduce receiver complexity.

FIG. 18 is a block diagram of an MLSE based on an approximation thateither (a) the channel is Gaussian, or (b) the channel has only ASEnoise is present and, therefore, v_(s)=0.5. In the MLSE of FIG. 3,samples from the ADC are input to the FFE 360. In FIG. 18, this inputpath is modified to have two possible paths, illustrated by the twoinputs 1810 and 1820 to the MUX. Path 1810 is the same as in FIG. 3.Samples from the ADC directly enter the FFE 360 (Gaussian channelassumed). Path 1820 is based on v_(s)=0.5 (ASE assumed). The MUX selectswhether the MLSE is run in Gaussian mode or ASE mode.

In the presence of only ASE noise (i.e., ASE mode), the random variabley^(0.5) is approximately Gaussian with the same variance for all thenoise-free levels s. This way, if the samples from the ADC are firstprocessed by the SQRT transformation 1830, the rest of the receiver(FFE, MLSE, Gaussian metrics, etc.) can be designed for “normal”operation (i.e., assuming a Gaussian channel). Note that this approachinvolves implementing the square root transformation of the receivedsamples before FFE. This could be implemented by using the ADCcalibration tables, but different track and hold offsets might degradethe accuracy This is because these offsets might originate “different”nonlinear transformations in each ADC. Therefore, in the example of FIG.18, an offset compensator 1840 is used prior to the SQRT transformation1830. The offset compensator may be implemented with little additionaloverhead since an offset compensator is used in the timing recoveryloop.

Furthermore, in the example of FIG. 18, the SQRT function 1830 isimplemented by a lookup table (LUT) addressed with the samples at theoutput of the offset compensator. In one embodiment, a table 1832 storesthe sqrt function y^(0.5). The table is used to generate the content ofthe LUT, as explained below. LUT data generation depends on the intervalof the input samples (i.e., the value of the AGC reference). Forexample, if the interval of the samples is between −y_(ref) and +y_(ref)(y_(ref)>0), the content of the LUT is given byLUT(y_(n))=sqrt(y_(n)+y_(ref)) with y_(n)>−y_(ref). In one design, thetiming recovery circuitry 340 (see FIG. 3) uses the samples without theSQRT transformation to avoid extra latency.

In a simplified approach, the SQRT function is approximated based on

$\begin{matrix}{{\left( {y + y_{\min}} \right)^{v} - \left( y_{\min} \right)^{v}} \approx \left\{ \begin{matrix}{{\alpha_{1}y},} & {y > 0} \\{{\alpha_{0}y},} & {y \leq 0}\end{matrix} \right.} & (20)\end{matrix}$

where α₀ and α₁ are proper constants. For channels with combined noise,α₁/α₀ varies between [0.5, 1]. Thus, further simplification can beachieved

α₁=(0.5+2^(−N) ¹ ), N ₁=‘1’(α₁=1),2,3,‘∞’(α₁=0.5)

α₀=(1.0+2^(−N) ⁰ ), N ₀=2,3,4,n _(bit)(α₀=1)  (21)

This approach can achieve significant gains with low complexity, andwithout the use of external information. The approach could also be usedwith external BER information by using exhaustive search of parametersN₀ and N₁. Internal adaptation is also possible.

An alternate approach uses the samples at the output of the FFE andfunctions with combined noise. This scheme is based on the cumulativemetric given by

$\begin{matrix}{\begin{matrix}{M = {\sum\limits_{n}{\frac{1}{2\zeta_{{\hat{s}}_{n}}}\left\lbrack {{T_{{\hat{s}}_{m}}\left( {\hat{y}}_{n} \right)} - {T_{{\hat{s}}_{n}}\left( {\hat{s}}_{n} \right)}} \right\rbrack}^{2}}} \\{\approx {\sum\limits_{n}{\frac{1}{2\zeta_{{\hat{s}}_{m}}}\left\lbrack {\left( {\hat{y}}_{n} \right)^{v_{{\hat{s}}_{n}}} - \left( {\hat{s}}_{n} \right)^{v_{{\hat{s}}_{n}}}} \right\rbrack}^{2}}}\end{matrix}\quad} & (22)\end{matrix}$

where the “hat” denotes samples at the FFE output. To reduce complexity,one value of v_(s), v, is used. One criteria to select this value isthat the variances ζ_(s) be approximately the same for all s. This way,the cumulative metric reduces to

M≈Σ _(n)[(ŷ _(n))^(v)−(ŝ _(n))^(v)]²  (23)

The value v may be estimated as follows. Let M_(2,s) be the conditionalsecond-order central moment of the samples before transformation. Then,it can be shown that ζ_(s)≈(v_(s))²s^(2(vs−1))M_(2,s). Therefore, asolution is given by

$\begin{matrix}{v \approx {1 + {0.5\frac{\log \left( {M_{2,1}/M_{2,0}} \right)}{\log \left( {S_{0}/S_{1}} \right)}}}} & (24)\end{matrix}$

where S₀=f(0, 0, . . . , 0), S₁=f(1, 1, . . . , 1), and M_(2,0) andM_(2,1) are the respective conditional second-order central moments ofthe noise. Note that v=1 for Gaussian channels (M_(2,1)=M_(2,0)). ForASE noise, it is possible to verify that M_(2,1)/M_(2,0)≈S₁/S₀ andv≈0.5. Note that this approach can address combined ASE and Gaussiannoise.

Unlike the SQRT approach shown in FIG. 18, this approach works with thesamples at the output of FFE 360. The transformation y^(v) may beimplemented with a LUT in the metric computation stage, prior to thesquare operation. The data in this LUT depends on the interval of thesamples at the output of the FFE. This range varies with the noise andthe channel (i.e., the FFE response).

In one implementation, it is estimated. If the interval of the samplesis known, e.g. [−y_(min), +y_(max)], (with y_(min), y_(max)>0), thecontent of the LUT is generated as follows

LUT(ŷ _(n))=(ŷ _(n) +y _(min))^(v) ŷ _(n) >−y _(min)  (25)

For an automatic adaptation of the parameters v and y_(min), thecomputation of the variances for two signal levels can be determined

$\begin{matrix}{{v \approx {1 + {0.5\frac{\log \left( {M_{,2,1}/M_{2,0}} \right)}{\log \left( {{\hat{S}}_{0}/{\hat{S}}_{1}} \right)}}}}{{\hat{S}}_{0} \approx {y_{\min} + g_{0}}}{{\hat{S}}_{1} \approx {y_{\min} + g_{1}}}{y_{\min} \approx {5\sqrt{M_{2,0}}}}} & (26)\end{matrix}$

where g₀ and g₁ are the minimum and maximum values of the channelestimator, respectively.

In one approach, the complexity in the computation of v may be reducedby using an approximation based on M_(2,0) and M_(2,1). For example, forASE and Gaussian limited cases, the approximation might be

v=0.5 if M _(2,1) /M _(2,0) >K _(0.5)

v=1.0 if M _(2,1) /M _(2,0) <K _(0.5)  (27)

where K_(0.5) is a programmable threshold level (e.g., K_(0.5)=1.5). Onepossible implementation uses a LUT-based approach similar to the onedescribed for FIG. 18, with different LUTs generated from differenttables (one for each value of v and y_(min)).

In yet another approach, the nonlinear function y^(v) can be implementedby using the approximation

y ^(v) ≈y ^(v) ^(ref) [1+(v−v _(ref))log(y)+0.5(v−v_(ref))²(log(y)²]  (28)

where y^(ref) is a tabulated reference. For the case v_(ref)=0.5, thenonlinear function reduces to

y ^(v) →K ⁻¹ y ^(0.5)+(v−0.5)y  (29)

where K is a given constant (e.g., K≈0.85). FIG. 19 is a block diagramillustrating calculation of the metric M of Eqn. 19, based onT_(s)(y)=y^(vs) and the approximations leading to Eqn. 29.

As yet another example, in a nondispersive SMF channel (e.g.,back-to-back test), the optimal detector reduces to a comparator with aproper threshold (offset). Thus, the optimal solution for anondispersive SMF channel is a MLSE with a shift of the baseline. Onnondispersive channels, the threshold can be analytically approximatedfrom the parameter v for equal noise power as follows

thr≈[0.5((S ₀)^(v)+(S ₁)^(v))]^(1/v) , S ₀,S₁>0,  (30)

where s₀ and s₁ are the noise-free signals. The parameter v may beestimated as described above.

Referring again to FIG. 3, FIGS. 13A-13C illustrate a block diagram ofone implementation of the timing recovery circuitry 340. As shown inFIG. 13A, this example includes circuitry to implement each of thefollowing: offset compensation 1310, timing phase correction 1320, phasedetection 1330, bandwidth control 1340 and a loop filter 1350. Theoutput of the timing recovery circuitry 340 drives the sampling clockgenerator 140, which in this example includes a numerically controlledoscillator (NCO) 1370 and phase interpolator 1380.

The timing recovery circuitry 340 operates as follows. The signal fromthe ADC may have a non-zero offset. The offset compensation 1310 iscircuitry that tracks this baseline wander and removes (or reduces) it.The timing phase corrector 1320 introduces a controlled amount of ISI byusing a filter with z-transform of F(z)=1−αz⁻¹, where |α|<<1 is adjusteddynamically to minimize the error signal from the multi-channelequalizer 350. The phase detector 1330 is based on a modified Muellerand Muller algorithm, based on pseudo-decisions derived directly fromthe input signal before equalization as shown in FIG. 13B.

As shown in FIG. 13C, the bandwidth control 1340 is achieved bymultiplying the signal from the phase detector 1330 by an adjustablegain G_(BW). The loop gain can be roughly estimated based on thedifference between a central tap and a lateral tap. If the central tapand lateral tap are closer in magnitude, this suggests that the loopgain is low. Conversely, if the central tap and lateral tap differ morein magnitude, this suggests that the loop gain is high. In one approach,the gain G_(BW) is set as G_(BW)=K_(BW)/|c₀−c₁|, where K_(BW) is aconstant set by the desired closed-loop bandwidth and c₀ and c₁ areestimates of the central tap and lateral tap, respectively. Loop filter1350 is a standard loop filter (e.g., a proportional-plus-integralfilter).

FIG. 14 is a block diagram of an alternate embodiment of the timingrecovery circuitry 340. In certain cases, the impulse response of thecommunications channel 110 may change significantly, including varyingbetween causal and anti-causal. For example, these types of changes mayoccur due to launch polarization or fiber movement in MMFs. Regardlessof the cause, these types of changes may seriously affect theperformance of timing recovery loops based on the Mueller and Mullerapproach.

FIG. 14 is similar to FIG. 13, except certain changes are made to allowbetter tracking of the received signal in time variant channels. Thereare two main differences. First, the timing phase corrector 1420 usesthree samples, rather than two. The filter is a “nearly all-pass” filterwith z-transform of F(z)=(1+αz)/(1+αz⁻¹)≈α+z⁻¹−αz⁻², with |α|<<1adjusted dynamically to minimize the error signal from the multi-channelequalizer 350. Second, a 3-tap FIR filter (pulse preprocessor 1425) isintroduced, with z-transform of G(z)=0.5+z⁻¹+0.5z⁻². This provides amore suitable pulse for phase detection based on the M&M algorithm. Italso allows the elimination of the automatic bandwidth control 1340.

In one approach, the timing recovery circuitry 340 is implemented in aparallel manner. Rather than processing one serial stream at 10G, theincoming data is decimated into eight parallel streams of 1.25G each.This allows the clocks (e.g., for the phase detector 1330) to run at the1.25G rate (actually 1.288 GHz clock) rather than at a 10G rate.

FIG. 15 is a block diagram of AGC 320. This example includes a maximumsample detector 1510 followed by a peak detector 1520, a one-bitquantizer 1530 and a counter 1540. The max sample detector 1510 receivesthe samples from the 16 ADC pipeline channels of the interleaved ADC 130(of which only eight are active at any one time) and detects the signalpeak from among the samples. The peak detector 1520 processes thisoutput using two filters with different time constants. If the signalpeak is greater than the peak detector output, then the filter with theshorter time constant is used. Otherwise, the filter with the longertime constant is used. This allows the peak detector output to increasequickly (fast attack) but degrade with a longer time constant (slowrelease).

The quantizer 1530 receives the output of the peak detector 1520. Itcompares the output to a reference value and generates a 1 or 0depending on whether the output is greater than or less than thereference. This 1/0 signal is used to adjust the gain of the PGA 120. Inone approach, the AGC is divided into a coarse gain and a fine gain. TheI/O signal is used initially to set the coarse gain and then used on acontinuous basis to set the fine gain via counter 1540.

FIG. 16 is a flow diagram illustrating a start-up sequence for thereceiver 115. The chip begins by resetting 1610 all functional blocks,including coarse and fine AGC 320, timing recovery 340 and multi-channelequalizer 350. In one implementation, the different ADC pipelines forthe interleaved ADC 130 are also calibrated during reset 1610. Reset1610 can occur upon power up or upon some error state. To reacquiresignal, the chip begins by setting 1620 the coarse AGC 320 followed bysetting 1625 the fine AGC 320. The timing recovery circuitry 340 thenacquires 1630 phase lock with the incoming signal.

The best delay search 1640 converges the multi-channel equalizer 350using the available cursor delays in the FFE 360. In an alternateembodiment, the best delay seach 1640 converges the equalizer 350 usingall the available delays in the FFE 360 and all the available delays inthe linear part of the channel estimator 380. For each convergence, themean squared error (MSE) obtained from error signal (Eqn. 6) is stored.Once all available delays are swapped, the best delay search 1640selects the delays which yielded the minimum MSE. The multi-channelequalizer 350 is then converged 1660 using the cursor delays determinedby the delay search 1640. After that, the chip operates in a normalmode.

The approach described has many advantages. For example, many of thefunctions have been chosen to allow maximum implementation on a DSP chip150. The 10G example results in an all-DSP (other than the analog frontend) electronic dispersion compensation receiver for the 10 GBASE-LRMapplication. The functions shown in DSP 150 of FIG. 3 are suitable forsingle chip implementation in standard 90 nm CMOS technology usingcurrent technology. Implementation using standard CMOS technology alsoallows the more complete integration of functionality. The use of aninterleaved ADC allows each ADC pipeline to operate at a slower clockrate. The use of the pipelined ADC architecture with open-loop residueamplifiers coupled with continuously calibrated, non-linear correctionresults in lower power consumption and faster operation withoutsacrificing accuracy. The use of digital calibration for the ADCpipelines allows much of this functionality to be moved to the DSP. Themulti-channel equalizer is tolerant to channel-to-channel variations inthe interleaved ADC front end. The receiver architecture is alsogenerally tolerant to significant amounts of channel dispersion andnonlinear channel response. The timing recovery circuitry allows forrobust operation even on time-varying channels, includinghigh-dispersion multimode fibers. Not all of the features must be usedin all implementations. Depending on the application, variousembodiments may include only some of the features and/or benefits.

The examples described above generally concern the receiver. However, inmany 10G and other applications, the communication links arebidirectional and the receiver and transmitter at each end of the linkare housed in a single transceiver module. In some applications, thesemodules are fixed to a host circuit board, and in other applicationsthey are “pluggable” modules that can be inserted into and removed froma cage (or socket) that is fixed to the host circuit card. Multi-SourceAgreements (MSAs) have been developed to achieve some degree ofinteroperability between modules from different manufacturers. ExampleMSAs include XFP and SFP+, in which the 10 Gbps electrical I/O interfaceto the host is serial, and X2, XPAK, and XENPAK, in which the 10 Gbpselectrical interface to the host is parallelized to four lanes in eachdirection. The receivers described above are well suited for inclusionin these types of transceiver modules.

FIG. 17 is a block diagram of a transceiver module that includes both atransmitter and a receiver as described above. This module uses the XAUIinterface (IEEE 802.3ae Standard, Clause 47: XGMII Extender Sublayer(XGXS) and 10 Gigabit Attachment Unit Interface (XAUI), published by theInstitute of Electrical and Electronic Engineers Inc, 2002) to the restof the system. The transmitter 105 includes the XAUI interfaceelectronics 1702, pre-driver 1706, laser driver 108 and laser 109. Thereceiver 120 includes photodetector 111, TIA 112 and then the componentsdescribed above. Shown in FIG. 17 are the PGA 120, track-and-hold units234, ADC 236-237, DSP 150 (which includes all of the components shown inFIG. 3), phase interpolator 1380 and sampling clock generator 140. Inthis example, all components shown inside block 1799 are implemented asa single integrated circuit. Analog components on the chip 1799 areidentified by the diagonal striping.

Although the detailed description contains many specifics, these shouldnot be construed as limiting the scope of the invention but merely asillustrating different examples and aspects of the invention. It shouldbe appreciated that the scope of the invention includes otherembodiments not discussed in detail above. For example, thefunctionality has been described above as implemented primarily inelectronic circuitry. This is not required, various functions can beperformed by hardware, firmware, software, and/or combinations thereof.Depending on the form of the implementation, the “coupling” betweendifferent blocks may also take different forms. Dedicated circuitry canbe coupled to each other by hardwiring or by accessing a common registeror memory location, for example. Software “coupling” can occur by anynumber of ways to pass information between software components (orbetween software and hardware, if that is the case). The term “coupling”is meant to include all of these and is not meant to be limited to ahardwired permanent connection between two components. In addition,there may be intervening elements. For example, when two elements aredescribed as being coupled to each other, this does not imply that theelements are directly coupled to each other nor does it preclude the useof other elements between the two. Various other modifications, changesand variations which will be apparent to those skilled in the art may bemade in the arrangement, operation and details of the method andapparatus of the present invention disclosed herein without departingfrom the spirit and scope of the invention as defined in the appendedclaims. Therefore, the scope of the invention should be determined bythe appended claims and their legal equivalents.

What is claimed is:
 1. A transceiver chip for communication at a datarate of 10 Gbps or higher over a fiber optic, the transceiver chipcomprising: a host interface for receiving digital data in electricalform at a data rate of 10 Gbps or higher, the received digital data in ahost format; a laser driver port for generating an electrical signalmodulated by the received data in electrical form, the electrical signalsuitable for driving a laser driver; transmit path circuitry coupledbetween the host interface and the laser driver port; a TIA port forreceiving an electrical signal from a transimpedance amplifier, theelectrical signal modulated by data at a data rate of 10 Gbps or higher;and receive path circuitry coupled between the TIA port and the hostinterface, the host interface further for transmitting digital data inelectrical form at a data rate of 10 Gbps or higher, the transmitteddigital data also in the host format.